Method and arrangements for determining information regarding an intensity peak position in a space-time volume of image frames

ABSTRACT

Determination of information regarding an intensity peak position in a space-time volume ( 360; 361 ) formed by image frames generated by an image sensor ( 331 ) from sensing of light reflected from a measure object ( 320 ) as part of light triangulation. The space-time volume ( 360; 361 ) is further associated with space-time trajectories relating to how feature points of the measure object ( 320 ) map to positions in the space-time volume ( 360; 361 ). A first hypothetical intensity peak position, HIPP 1,  ( 551   a;    651   a ) is obtained ( 701  in said space time volume ( 360; 361 ). A first space time analysis position, STAP 1,  ( 552   a;    652   a ) is computed ( 702 ) based on space-time analysis performed locally around the HIPP 1  ( 551   a;    651   a ) and along a first space time trajectory associated with the HIPP 1  ( 551   a,    651   a ). Said information regarding the intensity peak position is determined ( 703 ) based on the HIPP 1  ( 551   a;    651   a ) and the STAP 1  ( 552   a;    652   a ).

TECHNICAL FIELD

Embodiments herein concern a method and arrangements for determininginformation regarding an intensity peak position in a space-time volumeformed by image frames generated from light triangulation performed byan imaging system, more particularly are embodiments herein based onspace time analysis in the space-time volume.

BACKGROUND

Industrial vision cameras and systems for factory and logisticautomation may be based on three-dimensional (3D) machine vision, where3D-images of a scene and/or object are captured. By 3D-images isreferred to images that comprise also “height”, or “depth”, informationand not, or at least not only, information, e.g. intensity and/or color,regarding pixels in only two-dimensions (2D) as in a conventional image.That is, each pixel of the image may comprise such informationassociated with the position of the pixel in the image and that maps toa position of what has been imaged, e.g. the object. Processing may thenbe applied to extract information on characteristics of the object fromthe 3D images, i.e. 3D-characteristics of the object, and e.g. convertto various 3D image formats. Such information on height may be referredto as range data, where range data thus may correspond to data fromheight measurement of the object being imaged, or in other words fromrange or distance measurements of the object. Alternatively oradditionally the pixel may comprise information on e.g. materialproperties such as relating to the scattering of the light in the imagedarea or the reflection of a specific wavelength of light.

Hence, a pixel value may e.g. relate to intensity of the pixel and/or torange data and/or to material properties.

Line scan image data results when image data of an image is scanned orprovided one line at a time, e.g. by camera with a sensor configured tosense and provide image data, one line of pixels at a time. A specialcase of line scan image is image data provided by so called “sheet oflight”, e.g. laser-line, 3D triangulation. Laser is often preferred butalso other light sources able to provide the “sheet of light” can beused, e.g. light sources able to provide light that stays focused and donot spread out to much, or in other words, light that is “structured”,for example light provided by a laser or Light Emitting Diode (LED).

3D machine vision systems are often based on such sheet of lighttriangulation. In such a system there is a light source illuminating theobject with a specific light pattern, such as the sheet of light as thespecific light pattern, e.g. resulting in a light, or laser, line on theobject and along which line 3D characteristics of the object can becaptured, corresponding to a profile of the object. By scanning theobject with such a line, i.e. performing a line scan, involving movementof the line and/or object, 3D characteristics of the whole object can becaptured, corresponding to multiple profiles.

3D machine vision systems or devices that use a sheet of light fortriangulation may be referred to as systems or devices for 3D imagingbased on light, or sheet of light, triangulation, or simply lasertriangulation when laser light is used.

Typically, to produce a 3D-image based on light triangulation, reflectedlight from an object to be imaged is captured by an image sensor of acamera and intensity peaks are detected in the image data. The peaksoccur at positions corresponding to locations on the imaged object withthe incident light, e.g. corresponding to a laser line, that wasreflected from the object. The position in the image of a detected peakwill map to a position on the object from where the light resulting inthe peak was reflected.

A laser triangulating camera system, i.e. an imaging system based onlight triangulation, projects a laser line onto an object to createheight curves from the surface of the target object. By moving theobject relative to the cameras and light sources involved, informationon height curves from different parts of the target object can becaptured by images and then combined and used to produce a threedimensional representation of the target object.

This technique may be described as grabbing of images of the light,typically a laser line, when it is projected onto and reflected by theobject and then in the images extract positions of the reflected laserline. This is normally accomplished by identifying intensity peaks inthe image frames using any conventional peak finding algorithm, andtypically performed per column of the sensor. However, when there arediscontinuities, either geometrical, such as at the edge of a box, orintensity, such as a chess pattern, with dark to bright transitions, theconventional method suffers from artefacts due to that the laser linehas a width which will cover multiple pixels in the images.

One solution to reduce such artefacts and an alternative to usingconventional peak finding algorithms is a technique called space timetriangulation or space time analysis, see e.g. CURLESS B ET AL: “Betteroptical triangulation through spacetime analysis” COMPUTER VISION, 1995.PROCEEDINGS., FIFTH INTERNATIONAL CONFERENCE ON CAMBRIDGE, Mass., USA20-23 Jun. 1995, LOS ALAMITOS, Calif., USA, IEEE COMPUT. SOC, US, 20Jun. 1995 (1995-06-20), pages 987-994, XP010147003 ISBN:978-0-8186-7042-8. The idea is to analyze the time evolution of thestructured, e.g. laser, light reflections, following a point through thelaser line. It is utilized that the width, or profile, of the laser isimaged over time onto the sensor, corresponding to a Gaussian envelope.Thus, coordinates of an intensity peak can in principle be found bysearching for the mean of a Gaussian through sensor images followingtrajectories corresponding to how feature points of the object areimaged on, i.e. map to, sensor coordinates over time, in other words ina space time volume. The sensor position of the peak indicates a depthand the time indicates lateral position of the center of the laser. Thepaper illustrates the principle very well and also provide explanationof said artefacts associated with conventional peak finding algorithms.The technique presented in the paper can a bit simplified be describedby an algorithm where:

-   1) image frames are captured, the images forming a space time volume    of space time images (each image can be captured similarly as in the    case of conventional light triangulation),-   2) the space time images are skewed by a predetermined space time    angle,-   3) the statistics of the Gaussian light intensity distribution are    analyzed in the skewed coordinates and the mean or center positions    are identified, representing peak positions, and-   4) skewing back to the original coordinates is performed, that is,    the position of the peaks, in both the row and time dimension are    skewed back to the original coordinates.

Thereafter the positions can be used to produce a 3D image or model ofthe imaged object, in a similar manner as when peak positions have beenconventionally identified.

The finding of the peak positions in the space time volume can also bedescribed as analyzing light intensity distribution along trajectoriesin the space-time volume, where the trajectories in the paper areassumed to be straight lines inclined by the space time angle. Moregeneralized, the space time analysis approach can be described aslooking at a light intensity distribution along such trajectory in thespace time volume and find its center position instead of looking onlyat intensities in and find peaks per image frame. It can be realizedthat such trajectory can be considered to correspond to how a featurepoint of imaged object will move in the space time volume, being visiblein the images when it moves through the illuminant, i.e. the light, suchas a laser line. It is desirable to find a position in the space timevolume where the feature point passes through the center of the laserline.

Said paper teaches that, and how, the space time angle, and thus thetrajectories, can be calculated analytically based on a formula havinginter alia the geometrical and optical relation between the sensor andthe object as well as the motion of the object as input. However, in thepaper, for deriving the aforementioned formula for the space-time angle,some assumptions are made, e.g. that the sensor is orthographic and thatthe object moves with a constant velocity in relation to the measuringsystem during the execution of the optical triangulation. Theanalytically derived spacetime angle, and thus derived trajectories, donot account for secondary effects, such as for the projection via astandard imaging lens, secondary reflections and/or imperfections of theoptics connected to the sensor and is not suitable to apply in case ofvarying, i.e. not constant, velocity of the object in relation to themeasuring system.

EP 2 063 220 B1 discloses solutions to some problems with the originalspace time analysis technique and shows how trajectories in thespace-time volume of measure images may be established by adetermination method using a reference object, for use in a calibrationstage, with system settings etc. being the same as to be used formeasure objects. Hence, rather than deriving a new formula foranalytically determining the space-time angle or trajectories, thesolution is based on extension of trajectories determined from recordedmeasure data from the reference object. The approach allows for greaterflexibility and can also handle such secondary effects etc. mentionedabove. Different trajectories can be determined for different areas ofthe measure images and the space time volume. Furthermore, the methodcan be used for determining trajectories which are not linear.Embodiments presented in EP 2 063 220 B1 are based on assumption oftrajectory extension, determining an amount of artefacts when using theassumption and repeat with new assumption etc., until the amount isbelow a predetermined threshold value or has reached a minimum. Whentrajectories, or corresponding space time angle, have been determined,these can be followed to find out about light intensity distribution andidentify center position thereof in a space time volume of measureimages of a measure object. The main principle is the same as for theoriginal method disclosed in said paper, but since more practicallyuseful trajectories can be determined, the result is improved practicalapplicability of the space time analysis approach for intensity peakdetection.

However, solutions based on space time analysis as in said paper and inEP 2 063 220 B1 are associated with some drawbacks and practicalproblems. They are for example based on having access to the full spacetime volume and thus a full set of measure images forming it. Thesolutions are thereby difficult to implement near, or integrated with,the sensor. They also require processing of quite large amount of dataand are memory and computational heavy compared to conventional peakfinding algorithms. When speed is important, the prior art approachesmay not be suitable.

SUMMARY

In view of the above, an object is to provide one or more improvementsor alternatives to the prior art, such as providing a method based onlight triangulation and space time analysis that is more suitable forpractical implementation.

According to a first aspect of embodiments herein, the object isachieved by a method for determining information regarding an intensitypeak position in a space-time volume formed by image frames generated byan image sensor from sensing of light reflected from a measure object aspart of light triangulation. Said light triangulation being based onmovement of at least a light source and/or the measure object inrelation to each other so that at different consecutive time instants,different consecutive portions of the measure object are illuminated bythe light source and reflected light from the measure object is sensedby the image sensor. Each image frame of the space time volume isthereby associated both with a respective such time instant and with arespective portion of the measure object from which the image sensorsensed light at the respective time instant. Said space-time volume isfurther associated with space-time trajectories relating to how featurepoints of the measure object map to positions in the space-time volume.It is obtained a first hypothetical intensity peak position (HIPP1) insaid space time volume. It is then computed a first space time analysisposition (STAP1) based on space-time analysis performed locally aroundthe first hypothetical intensity peak position and along a first spacetime trajectory. The first space time trajectory being a space timetrajectory of said space time trajectories that is associated with, i.e.pass through, the first hypothetical intensity peak position. Saidinformation regarding the intensity peak position is determined based onthe HIPP1 and the computed STAP1.

According to a second aspect of embodiments herein, the object isachieved by a computer program comprising instructions that whenexecuted by one or more processors causes one or more devices to performthe method according to the first aspect.

According to a third aspect of embodiments herein, the object isachieved by a carrier comprising the computer program according to thesecond aspect.

According to a fourth aspect of embodiments herein, the object isachieved by one or more devices for determining information regarding anintensity peak position in a space-time volume formed by image framesgenerated by an image sensor from sensing of light reflected from ameasure object as part of light triangulation. Said light triangulationbeing based on movement of at least a light source and/or the measureobject in relation to each other so that at different consecutive timeinstants, different consecutive portions of the measure object areilluminated by the light source and reflected light from the measureobject is sensed by the image sensor. Each image frame of the space timevolume is thereby associated both with a respective such time instantand with a respective portion of the measure object from which the imagesensor sensed light at the respective time instant. Said space-timevolume is further associated with space-time trajectories relating tohow feature points of the measure object map to positions in thespace-time volume. Said one or more devices are configured to obtain afirst hypothetical intensity peak position (HIPP1) in said space timevolume. Said one or more devices are configured to are furtherconfigured to compute a first space time analysis position (STAP1) basedon space-time analysis performed locally around the HIPP1 and along afirst space time trajectory. The first space time trajectory being aspace time trajectory of said space time trajectories that is associatedwith the HIPP1. Moreover, said one or more devices are configured todetermine said information regarding the intensity peak position basedon the HIPP1 and the computed STAP1.

In some embodiments, the determination of the information regarding theintensity peak position comprises computation of a first positiondifference, PD1, in the space-time volume, between the HIPP1 and thecomputed STAP1. If the computed PD1 is below or equal to a certainthreshold value, that may be predetermined, there is provision of theHIPP1 as a determined intensity peak position. If PD1 instead is abovesaid threshold value, a new HIPP2 may be obtained, e.g. selected, closerto the STAP1. By one or more iterations, according to some embodiments,further STAP(s) and PD(s), e.g. a STAP2 and PD2 based on space-timeanalysis locally around HIPP2, can be provided, the PD2 compared to thethreshold, etc. This way improved HIPPs can be accomplished and theresult can be as good or even better as from conventional space timeanalysis. At the same time embodiments herein can be implemented moreefficiently than conventional space-time analysis, with less resources,and are better adapted for real time, or near real time, execution.There is no need to have access to a complete space-time volume of imagedata to operate on, it is sufficient with image data locally around eachHIPP. This facilitates implementation in close connection with the imagesensor, operating on subsets of image frames provided by the imagesensor and on image data of partial space-time volumes formed by theseimage frames.

In some embodiments, the determination of the information regarding theintensity peak position comprises provision of a comparison between theHIPP1 and the computed STAP1 as a reliability indicator indicating howreliable the HIPP1 is as intensity peak position. These embodiments maybe particularly advantageous when HIPP1 has been determined by aconventional peak finding algorithm since it provides valuableinformation about how good or bad the algorithm was in finding reliablepeaks and/or that can be used to identify peak positions that are notreliable so they e.g. can be excluded and not used, or be rectifiedand/or replaced.

Thus embodiments herein not only enable improved and more correct peakpositions than possible through conventional peak finding algorithmsthanks to that the embodiments are based on space-time analysis, theyalso facilitate practical implementation and can additionally, oralternatively, be used to find out about reliability or quality of apeak position determined according to a conventional peak findingalgorithm, or determined based on embodiments herein with iterations.

BRIEF DESCRIPTION OF THE DRAWINGS

Examples of embodiments herein are described in more detail withreference to the appended schematic drawings, which are brieflydescribed in the following.

FIG. 1 schematically illustrates an example of a prior art imagingsystem that also can be used to provide images relevant for embodimentsherein.

FIG. 2 schematically shows a prior art example of a space time volumeformed by images frames and with a space time trajectory for space timeanalysis.

FIGS. 3A-B schematically illustrates an simplified example of a animaging system that can be configured to carry out embodiments herein.

FIG. 4 is a schematical illustration to enhance understanding of aprinciple behind space time analysis and embodiments herein.

FIGS. 5A-B are a first example that schematically shows and is used toexplain how hypothetical intensity peak positions (HIPPs) and space timeanalysis position (STAPs) can be provided and used in an iterativemanner according to some embodiments herein.

FIGS. 6A-B are a second example showing similar views as in FIG. 6 andthe first example but for illustrating what it may look like when afirst HIPP used is a poor starting point.

FIGS. 7A-C are flowcharts schematically illustrating embodiments of amethod based on the above and according to embodiments herein

FIGS. 8A-B show a result from light triangulation when a conventionalpeak finding algorithm has been used for qualitative comparison with aresult when embodiments herein have been applied.

FIG. 9 is a schematic block diagram for illustrating embodiments of howone or more devices may be configured to perform the method and actionsdiscussed in relation to FIGS. 8A-B.

FIG. 10 is a schematic drawing illustrating some embodiments relating tocomputer program and carriers thereof to cause device(s) to perform themethod and actions discussed in relation to FIGS. 8A-B

DETAILED DESCRIPTION

Embodiments herein are exemplary embodiments. It should be noted thatthese embodiments are not necessarily mutually exclusive. Componentsfrom one embodiment may be tacitly assumed to be present in anotherembodiment and it will be obvious to a person skilled in the art howthose components may be used in the other exemplary embodiments.

FIG. 1 schematically illustrates an example of such type of imagingsystem as mentioned in the Background, namely an imaging system 100, for3D machine vision, based on light triangulation for capturinginformation on 3D characteristics of target objects. The system can beused for providing images that embodiments herein, described furtherbelow, can operate on. The system 100 is in the figure shown in asituation of normal operation, i.e. typically after calibration has beenperformed and the system is thus calibrated. The system 100 isconfigured to perform light triangulation, here in the form of sheet oflight triangulation as mentioned in the Background. The system 100further comprises a light source 110, e.g. a laser, for illuminatingobjects to be imaged with a specific light pattern 111, in the figureexemplified and illustrated as a sheet of light. The light may, but notneed to be, laser light. In the shown example, the target objects areexemplified by a first measure object 120 in the form of a car and asecond measure object 121 in the form of a gear wheel construction. Whenthe specific light pattern 111 is incident on an object, thiscorresponds to a projection of the specific light pattern 111 on theobject, which may be viewed upon as the specific light pattern 111intersects the object. For example, in the shown example, the specificlight pattern 111 exemplified as the sheet of light, results in a lightline 112 on the first measure object 120. The specific light pattern 111is reflected by the object, more specifically by portions of the objectat the intersection, i.e. at the light line 112 in the shown example.The measuring system 100 further comprises a camera 130 comprising animage sensor (not shown in FIG. 1 ). the camera and image sensor arearranged in relation to the light source 110 and the objects to beimaged so that the specific light pattern, when reflected by theobjects, become incident light on the image sensor. The image sensor isan arrangement, typically implemented as a chip, for converting incidentlight to image data. Said portions of the object, which by reflectioncauses said incident light on the image sensor, may thereby be capturedby the camera 130 and the image sensor, and corresponding image data maybe produced and provided for further use. For example, in the shownexample, the specific light pattern 111 will at the light line 112 on aportion of the car roof of the first measure object 120 be reflectedtowards the camera 130 and image sensor, which thereby may produce andprovide image data with information about said portion of the car roof.With knowledge of operating conditions and geometries of the measuringsystem 100, e.g. how image sensor coordinates relate to worldcoordinates, such as coordinates of a coordinate system 123, e.g.Cartesian, relevant for the object being imaged and its context, theimage data may be converted to information on 3D characteristics, e.g. a3D shape or profile, of the object being imaged in a suitable format.The information on said 3D characteristics, e.g. said 3D shape(s) orprofile(s), may comprise data describing 3D characteristics in anysuitable format.

By moving e.g. the light source 110 and/or the object to be imaged, suchas the first measure object 120 or the second object 121, so thatmultiple portions of the object are illuminated and cause reflectedlight upon the image sensor, in practice typically by scanning theobjects, image data describing a more complete 3D shape of the objectmay be produced, e.g. corresponding to multiple, consecutive, profilesof the object, such as the shown profile images 140-1-140-N of the firstmeasure object 120, where each profile image shows a contour of thefirst object 120 where the specific light pattern 111 was reflected whenthe image sensor of the camera unit 130 sensed the light resulting inthe profile image. As indicated in the figure, a conveyor belt 122 orsimilar may be used to move the objects through the specific lightpattern 112, with the light source 110 and the camera unit 130 typicallystationary, or the specific light pattern 111 and/or the camera 130 maybe moved over the object, so that all portions of the object, or atleast all portions facing the light source 110, are illuminated and thecamera receives light reflected from all parts of the object desirableto image.

As understood from the above, an image frame provided by the camera 130and its image sensor, e.g. of the first measure object 120, maycorrespond to any one of the profile images 140-1-140-N. As mentioned inthe Background, each position of the contour of the first object shownin any of the profile images 140-1-140-N are typically determined basedon identification of intensity peaks in image data captured by the imagesensor and on finding the positions of these intensity peaks. The system100 and conventional peak finding algorithms are typically configured toin each image frame search for an intensity peak per pixel column. Ifsensor coordinates are u, v and for example u, as indicted in thefigure, corresponds to pixel positions along rows in the image sensorand v corresponds to pixel positions along columns, there is for eachposition u of an image frame searched for peak position along v and theidentified peaks in an image frame may result in one such clean profileimage as shown in the figure, and the total of image frames and profileimages can be used to create a 3D image of the first object 120.

FIG. 2 schematically illustrates a set of image frames or measureimages, e.g. generated by the imaging system 100. It is here as exampleshown four images or image frames IM1-IM4 at four time instants, e.g.t1-t4, respectively. Each image frame may be generated by the imagesensor of the camera 130. The image frames may image a measure object,e.g. the measure object 120, and each image frame may thus containinformation in the form of sensed light that can be used to create oneof the profile images 140. The images IM1-1M4 are stacked and form aspace-time volume (SW) 360 of image frames. Each image frame IM has animage transversal dimension u and an image longitudinal dimension v,hence the space-time volume 360 has three dimensions, the time dimensiont being the third dimension. Since the measure object, e.g. the firstmeasure object 120, moves in relation to the camera 130 and light source110 during the generation of the measure images IM1-1M34, an examplefeature point 240 of the imaged measure object will map to an examplespace time trajectory 262 in the space-time volume 360 with (u, v, t)coordinates. At some positions in the STV 360, reflections from theexample feature point have resulted in higher intensities and formed anintensity peak. Note that FIG. 2 is only to exemplify and visualize somerelations used in space time analysis. In practice a space time volumewith images of a complete measure object may comprise hundreds of imageframes and at least many more than just four. Also, in the example,trajectories may be parallel and straight and not change in u, wherebyeach one, just as the trajectory 262 shown as an example in the figure,can be described by an angle, which is a situation used in the originalspace time analysis paper mentioned in the Background. However, asindicated above, in practice, a trajectory may change position also in uand need not be straight through the space time volume. Suchtrajectories can e.g. be determined in a calibration stage using areference object, as also discussed in the Background.

FIG. 3A schematically illustrates an exemplary imaging system 300, basedon light triangulation for capturing information on 3D characteristicsof one or more measure objects. The imaging system 300 may be used forimplementing embodiments herein. The shown system corresponds to a basicconfiguration with one light source 310 and one camera 330, arranged atcertain positions, respectively, is suitable and/or configured for lighttriangulation, e.g. laser triangulation. The system 300 may thuscorrespond to the system 100 in FIG. 1 but configured to performaccording to embodiments herein. There is shown a measure object 320,that may correspond to the first measure object 120, and that is shownlocated at least partly within field of view 331 of the camera 230. Thelight source 210 illuminates the measure object with light 311 in theform of a specific light pattern, e.g. a sheet of light and/or laserline that is reflected by the measure object and the reflected light iscaptured by the camera 330. The measure object 320 is illuminated andimages may be captured, as in conventional light triangulation. Thesystem may e.g. be configured to move the measure object 320, such as bymeans of a conveyor belt, so it thereby becomes completely illuminatedby light from the light source 310, and/or the system may be configuredto move the light source and/or camera with sensor to accomplish thesame thing. The light source 310 and camera 330 are typically arrangedat fix positions in relation to each other.

The camera 330 may be a prior art camera, e.g. correspond to the camera130 in the system 100 of FIG. 1 and may comprise an image sensor 331that may be the same or similar image sensor discussed above in relationto FIG. 1 . Image frames and/or information derived from image framesprovided by the camera 330 and the image sensor 331 may be desirable totransfer, e.g. transmit, for further processing outside the camera 330,e.g. to a computing device 301, such as a computer or similar. Suchfurther processing may additionally or alternatively be performed by aseparate computing unit or device (not shown), i.e. separate from theimage processor 131, but still comprised in, e.g. integrated with, thecamera 330, or a unit comprising the camera 330.

Before describing embodiments herein in detail, the prior art andproblems indicated in the Background will be elaborated upon and someprinciples that embodiments herein are based on will be introduced andexplained.

FIG. 3B schematically illustrates a set of image frames. e.g. generatedby the imaging system 300 when imaging the measure object 320. The shownimage frames form a space time volume, SW, 360 that may comprise imageframes from a full scan of the measure object 320. In the figure it isalso shown example of a partial space time volume (pSTV) 361 that is asubset of the total, or full, STV 360. The pSTV may e.g. be formed by apredetermined number of sequential image frames of the total space timevolume, e.g. a subsequence of the total sequence of image frames formingthe STV 360, e.g. image frames IM_(i−K) . . . IM_(i+L), around, such ascentered around, an image frame IMi of the total of image frames, asillustrated in the figure for a situation where L=K. For example, if itis assumed that the total space time volume for an imaged measureobject, e.g. STV 360, is formed by 500 image frames IM1-1M500, a firstpSTV can be formed from a subsequence of image frames IM1-1M15, a secondSTV be formed from a subsequence of image frames 1M2-1M16, a third from1M3-1M17, etc.

In the pSTV 361 it is shown an example position 340, e.g. located inIM_(i), and also a partial space time trajectory 362 that is atrajectory for an example feature point 340 that thus map to positionsin the pSTV 361 and in the figure is shown mapping to a position in IMi.It is named a partial trajectory since it only relates to image data ofthe partial STV 362, but can be based on and be part of space timetrajectories for the STV 360, i.e. the whole STV, and e.g. be part of orformed based on a trajectory of the STV 360 that passes through the pSTV361.

In the prior art teachings mentioned in the Background, a full stack ofimage frames from the sensor is used, e.g. the complete STV 360, whereasembodiments herein are applicable to part of the stack and e.g. the pSTV361. Embodiments herein can beneficially be implemented in a pipelinedmanner to cover a full stack of image frames of a complete measureobject.

Positions of intensity peaks located in a pSTV can be determined basedon embodiments herein even though embodiments herein are based on theprior art space time analysis principle. At the same time when intensitypeak positions and/or information about intensity peak positions aredetermined for the first pSTV according to embodiments herein, imageframe(s) of a second subset can be sensed and provided by the imagesensor. Hence, less data is needed to be stored and provided at the sametime and positions can be identified before all image frames and thefull space time volume is available.

FIG. 4 is a schematical illustration to enhance understanding ofprinciples and relations behind space time analysis and embodimentsherein. The figure can be considered representing a subset orsubsequence of a space time volume of a sequence of image frames fromlaser triangulation, such as resulting from operation of the imagingsystem 300 and imaging of the measure object 320. The subsequence maye.g. be part of the STV 360 and here involves three image framesIM_(i−1) . . . IM_(i)+1. Assume this subsequence has captured a featurepoint 440 of the measure object when it moved through a laser line 431,or laser sheet of light. The feature point 440 will follow a trajectoryin the space time volume and be visible where it was illuminated.However, since the laser line 431, as shown in the figure, has a width,it will illuminate the feature point 440 not only at a single occasionor point in time. The intensity will also vary over the width asattempted to be illustrated by the bell shape of the laser line 431 inthe figure. When light is reflected back from the measure object, thelight distribution can be affected as well.

The feature point 440 can thus be seen as sampling the laser line 431over its width, resulting in that the feature point 440 in the imagesIM1-3 will be sampled at 3 different light intensities depending on thelight illuminating the feature point 440. The intensity samples arecaptured at time instants t_(i−1), t₊₁, i.e. when the image frames werecaptured as schematically illustrated in the figure. The same featurepoint 440 may thus give rise to 3 intensity values at differentpositions in three different consecutive image frames.

Note that “up” in the figure is intensity level, not position in theimage frame, or sensor plane, although these positions generally changeas well when the imaged feature point 440 follows a trajectory in thespace time volume through said image frames. The three image frames areshown just to indicate belonging of the feature point 440 samples tothese image frames. Regarding a space time trajectory that a featurepoint moves along, an imaging system may be arranged so that featurepoints moves in the real world e.g. only or substantially only in ay-direction, e.g. as indicated in FIG. 1 , in addition to that it alsomoves in time of course. Generally this results in that the featurepoint in sensor coordinates and the space time volume moves in both uand v, in addition to in time t, i.e. its space time trajectory maychange in all coordinates (u, v, t).

As further realized from FIG. 4 , the actual position in time when thefeature point 440 passes the center of the laser line is between thetime instants t_(i) and t_(i+1) associated with image frames IM_(i) andIM_(i+1), respectively. The center of the laser line is thus notdirectly sampled but can still be identified in the space time volume.Reconstruction when and where a true center passing occurs, even whenthis is between frames, can be accomplished by looking at multipleframes. While not limited to looking only symmetrically around thecenter point, this may make sense for accuracy of the detection. It canbe understood that it is typically required actual samples from at least3 image frames to be able to reconstruct the signal, i.e. lightdistribution in the space time volume. In practice, 5 or 7 image framesmay be used as minimum, but more image frames, such as 9, 11, 15 or 31image frames with image data may be desirable to use to form each pSTV.

With knowledge of the space time trajectories in the space time volumeit is possible to follow or track feature points sampling the illuminantin directions with better knowledge of the light distribution and wherethe light distribution can be utilized to identify a center position.This is utilized in prior art space time analysis to find a centerposition of the light distribution, even though the actual center is notdirectly sampled in any image frame. For embodiments herein, obtaininginformation about a trajectory for a hypothetical intensity peakposition provides information on which samples to use for reconstructingthe light distribution and e.g. find its center position.

As already mentioned, conventional space time analysis is performed overthe whole space time volume and a found center of light distributionalong a space time trajectory can be assumed to represent an actual peakposition.

In embodiments herein, however, the starting point is a firsthypothetical intensity peak position (HIPP), i.e. a first HIPP or HIPP1,and the space time analysis is performed only locally and using aportion of the full space time volume. A center position identifiedthrough space time analysis, i.e. a space time analysis position (STAP)is not assumed to be a correct peak position for a feature point. TheSTAP is instead rather used for comparison with and to evaluate theHIPP. The position difference between them can be seen as a qualitymeasure of the HIPP. If the measure, e.g. difference, indicates that theHIPP is too far away, it can be dismissed and/or a new refined HIPP canbe provided based on the earlier HIPP and its STAP, i.e. the positionidentified through space time analysis. This can be repeated, oriterated, getting hypothetical peak positions that are increasinglybetter. Some STAPs will e.g. correspond to artefacts due to a poor firsthypothetical intensity peak position. However, embodiments herein makesit possible to identify such “undesired” or false peak positions so theycan be avoided to be used, or at least can have less impact when thepeak positions are used to form a 3D image of a measure object. However,real peak positions can be identified through said iterations andrefined hypothetical peak positions (improved hypotheses) with resultssimilar as for conventional space time analysis, i.e. to find betterpeak positions than possible with only a conventional peak detectingalgorithm. However, in contrast to the prior art, embodiments herein atthe same have the advantage that they do not require a complete spacetime volume to be available until space time analysis is performed andcan be utilized. It suffices with availability of and operation on localdata from a subsequence of image frames that form a partial space timevolume as described above.

Hence, embodiments herein can be considered based on using hypotheticalintensity peak positions (HIPPs), e.g. starting with a first HIPP(HIPP1) that preferably is a peak position identified by a conventionalintensity peak finding algorithm, e.g. one for finding a peak positionin a column of an image frame. Then the HIPP1 is evaluated based on theresult from spacetime analysis that as mentioned can be performedlocally around the HIPP1 and thus only need data from said subset ofimage frames and thus from locally around HIPP1. Space time trajectoriesfor the space time analysis can be determined as in the prior art andfully or partly predetermined space time trajectories can be used, e.g.determined or predetermined by using a reference object during acalibration stage prior to applying embodiments herein.

If the space time analysis indicates that a HIPP is not reliable enough,e.g. not accurate enough or of too low quality, which e.g. can beidentified by a too large position difference (PD) in space time betweenthe hypothetical intensity position and a space time analysis position(STAP). The STAP typically being a center position of light distributionfound from space time analysis along the trajectory passing the HIPP. Anew better hypothesis point can then be selected based in the result,new space time analysis be performed etc. That is, by iterativerefinement, using space time analysis that can be performed onlylocally, e.g. using image data from a window around a HIPP, improvedpeak positions can be determined compared to conventional peak findingalgorithms, with benefits similar as from application of space timeanalysis as in the prior art. These benefits include the benefit ofusing sampling points in time and space so that intensity sampling ofthe laser line becomes more consistent and doesn't suffer, or sufferless, from artefacts due to intensity variations and/or surfacediscontinuities in the measure object.

Additionally, embodiments herein can be used to provide a measureindicating the reliability and/or accuracy of a hypothetical peakposition, e.g. HIPP1, provided based on a conventional peak findingalgorithm or a refined hypothetical peak position according to someembodiments herein. The measure can be seen as a quality measure thate.g. can be used to discard data that result in hypothetical peakpositions indicated as unreliable, incorrect and/or undesirable, whichcan indicate that they likely result from noise, reflections, or otherphenomena known to cause artefacts in the light triangulation images.Undesired peak positions, e.g. due to artefacts resulting from secondaryeffect such as reflections, noise, etc. will likely never reach a stablestate, even after a number of iterations as applied in some embodimentsherein.

Embodiments herein may e.g. be used to provide such quality measure perposition in a full space time volume for a measure object, whichmeasures can be used to remove space-time-inconsistent positions,enabling better and more accurate 3D images of the measure object.

It was mentioned above that results with embodiments herein are similaras in conventional space time analysis. A bit surprisingly it has beenfound that the method according to embodiments even can have improvedaccuracy over conventional space time analysis. The improvement isbelieved to be attributed that embodiments herein do not require to skewthe space time volume, and then back again, as in conventional spacetime analysis.

FIGS. 5A-B is a first example that schematically shows and will be usedto explain how hypothetical intensity peak positions (HIPPs) and spacetime analysis positions (STAPs) can be provided and used in an iterativemanner according to some embodiments herein, e.g. until a HIPP becomesgood enough to be used as a determined intensity peak position.

FIG. 5A is a bitmap picture that shows part of an image frame, say ani:th image frame, IMi, that has captured part of reflected laser linefrom a measure object, such as the measure object 320 or 120. The imageframe IMi is part of a space time volume (STV), more particularly partof a sequence of image frames forming a partial STV (pSTV) that is partof a full or complete STV as described above. The image frame IMi mayhere be part of a subsequence involving e.g. 7 image frames before and 7image frames after IMi. In other words, image data used may be fromimage frames IM_(i−7) to I_(i+7).

The captured laser line is visible in FIG. 5A and has a width withvarying intensity. In a column of IMi a first HIPP1 551 a has beenobtained, e.g. through a conventional peak finding algorithm, shown as acircle in FIG. 5A and also shown in FIG. 5B, in the “start” diagram.FIG. 5A also schematically shows sample points marked with thin crossesthat are sample positions along a space time trajectory that passesthrough HIPP1 in IMi. Note that the trajectory, represented by the lineof thin crosses in FIG. 5A, is shown as a projection in IMi and the timeinstant associated with this image frame, but that the trajectoryactually is a trajectory in the pSTV and also change position in time.The shown trajectory is just for visualizing the trajectory principle.Also note that when moving along the trajectory and when it e.g. passesnext image frame, IMi+1, what is shown in the upper picture should bereplaced and be visualized by image data from IMi+1, i.e. what is shownin FIG. 5A is only for a certain time instant in the space-time volume.

In FIG. 5A there is also shown a star corresponding to a position alongthe shown trajectory where a sufficiently good HIPP, here a HIPP3 551 c,has been selected after 2 iterations according to some embodimentsherein and described in the following.

Note that since the space time volume here considered is partial, thetrajectory obtained for HIPP1 is a partial trajectory as well, e.g.corresponding to the partial space time trajectory 362. As alreadymentioned above, information about trajectories as such can be obtainedas in the prior art and this information may thus be obtained beforeapplication of embodiments here. Trajectories relevant for embodimentsherein can thus be considered predetermined at the time embodimentsherein are applied and partial when applied to a partial space timevolume.

Further note that the sampling in the space time volume is typicallymade by interpolation. That is, the raw data, i.e. image data of theimage frames provided by the image sensor, have samples for “integer”(u,v,t) positions and resampling is made to obtain the subpixel position(u,v,t) points that may be used in the processing described in thefollowing and relevant for embodiments herein. In other words, positionsand coordinates in the space time volume and used in the processing neednot be at time instants and positions of the captured image frames thatcorrespond to exact sensor pixel positions and time instants associatedwith captured image frames, but can be located between these. This issimilar as for space time analysis in the prior art.

In any case, the space time trajectory associated with HIPP1 551 apasses through HIPP1 551 a in IMi and also through image frames IM¹⁻⁷ toIM_(i+7). If sampled intensities along the trajectory are plotted theresult is the “start” diagram of FIG. 5B that thus shows a light, orrather intensity, distribution along the trajectory passing throughHIPP1 551 a. HIPP1 551 a can e.g. be seen as corresponding to the pointplotted in FIG. 3B for the example feature point 340 if the trajectorythrough HIPP1 would correspond to the partial space time trajectory 362.The center position of the light distribution shown in the start diagramof FIG. 5B is marked with a cross in this figure and corresponds to afirst position according to space time analysis, i.e. a first space timeanalysis position, STAP1, 552 a. This position is in the example locatedbetween IMi and IMi+1 in time. As also can be seen in the figure thereis a position difference (PD), viz. a first position difference, PD1,553 a, between HIPP1 and STAP1. The difference is in the lowest diagramof FIG. 5A represented by a time difference along the horizontal axis.The difference is here too large for HIPP1 to be considered a reliableor accurate peak position since according to space time analysis theSTAP1 should be at the HIPP1 if the HIPP1 would be an actual, i.e.correct, peak position. What is considered being a too large differencefor a certain system and setup etc. can be found out from testing androutine experimentation. When a time difference and threshold is used,the threshold may be a fraction of the time between two consecutiveimage frames. A threshold corresponding to a max tolerated differencecan be obtained prior to application of embodiments herein. For theworking principle of embodiments herein a certain threshold can thus beassumed, and in practice e.g. be predetermined, at the time embodimentsherein are applied.

Note that since a complete space time volume is not analyzed or may noteven yet be available, it cannot be assumed that a found center of alight distribution, i.e. a STAP, as such, is an actual peak position.Instead a new HIPP is obtained if HIPP1 is not considered good enough.It has been found that it is generally more effective to select the nextHIPP, in the shown example HIPP2 551 b, as a position located along saidtrajectory through HIPP1 and closer to the identified center position ofthe light distribution, i.e. here STAP1, but not directly at or beingthis center position. For example, HIPP2 551 b is preferably, and asshown in the figure, selected as a position between HIPP1 and STAP1.When HIPP2 has been selected as a position between HIPP1 and theposition of STAP1 in the “start” diagram of FIG. 5B.

In a first iteration 1, HIPP2 is then used instead of HIPP1 and is shownin the center of the “iteration 1” diagram of FIG. 5B. This since, inthe example, image data is resampled around HIPP2 and along thetrajectory through HIPP2, whereafter basically the correspondingprocedure as described above can be repeated but for HIPP2 instead ofHIPP1. In the shown example, there is thus used resampled values for theimage data in the “iteration 1” diagram compared the “start” diagram,which explains that the intensity value shown at HIPP1 is not exactlythe same in the two diagrams. The resampled trajectory for HIPP2,corresponding to a second partial trajectory with samples from imagedata centered around HIPP2 results in the shown “iteration 1 diagram” ofFIG. 5B.

The result is thus a new, second, light distribution along thetrajectory passing HIPP2 and with HIPP2 551 b in the center, as shown inthe “iteration 1” diagram of FIG. 5B.

In said second iteration, a center of the light distribution aroundHIPP2 is identified, i.e. a STAP2 552 b is found, and a positiondifference PD2 553 b obtained and compared to the threshold, i.e. asimilar manner as above for HIPP1. It can be seen that the differencenow is smaller but still identifiable in the figure. With PD2 also toolarge according to the threshold, yet another similar iteration,iteration 2, is performed resulting in a HIPP3 551 c and third lightdistribution as shown in the “iteration 2” diagram. This time it can beseen that there is hardly any difference between the position of HIPP3and a STAP3 552 c, e.g. the center of the third light distribution. Thisdifference, i.e. PD3 553 c, is in the example small enough and below thethreshold. HIPP3 is therefore considered a reliable intensity peakposition or a peak position of high quality, i.e. one for further useand that should correspond to an imaged actual feature point of themeasure object located at accurate space time coordinates. The peakposition according to HIPP3, i.e. the star in the upper picture of FIG.5 , thus has a much higher quality than the initial, first HIPP1, i.e.the circle.

It can also be noted that the shown light distributions have Gaussianlike shapes, as expected when space time analysis is applied alongtrajectories.

As mentioned above, interpolation is used in the space time volume andpositions used are thus not necessarily positions in a certain imageframe forming the space time volume. The markers on the horizonal axisof the diagrams of FIG. 5B are thus not necessarily markers that markpositions in image frames, although the distance between the markerscorresponds to the time between image frames. If HIPP1 is a position inan image frame and since it is at the center position, e.g. 0 position,in the “start” diagram of FIG. 5B then the markers of the “start”diagram correspond to space time volume positions in time where imageframes are located. However, in the “iteration 1” and “iteration 2”diagrams, centered around HIPP2 and HIPP3, the markers will typicallynot indicate positions of the image frames.

As already mentioned above, the light distributions of the diagrams inFIG. 5B are shown with the respective HIPP centered in time. This can beseen as each HIPP is centered in time in a partial space time volume.Another way of viewing this can be to consider there being applied atime window around the HIPP, i.e. with the HIPP positioned in thecenter. The time window can be considered to determine pSTV and imagedata to be used with the HIPP. The space time volume is then sampledwithin this window, or in the determined pSTV, along the trajectorythrough the HIPP.

FIGS. 6A-B is a second example showing similar views as in FIGS. 5A-Band the first example, but for illustrating what it may look like when afirst HIPP, here a HIPP1 651 a, is a poor starting point and e.g. faraway from any substantial intensity peak position. The main principlesas explained above in relation to FIG. 6 are the same. The first HIPP1is also here also represented by a circle in the upper bitmap picturethat may correspond to an image frame IMi. A last HIPP, here HIPP3 651c, explained further below, is shown as a star in the upper picture.

Similar as for the first example there is used image data from asequence of image frames around HIPP1, the image frames forming a pSTVof a complete STV with HIPP1 preferably in the middle of the imageframes forming the pSTV. For example, the pSTV may be formed by imageframes IM_(i−7) to IM₊₇ if it is assumed that IMi is the image frameshown in the upper bitmap picture and HIPP1 is located in this imageframe. The iterative principle for reaching HIPP3 in the upper diagramis the same as in the first example of FIGS. 5A-B. Hence there is a PD1653 a between HIPP1 651 a and a STAP1 652 a, a PD2 653 b between a HIPP2651 b and a STAP2 652 b, and a PD3 653 a between HIPP3 651 c and a STAP3652 c.

In FIGS. 6A-B it can be seen that although the initial positiondifference PD1 653 a in the “start” diagram was larger than the laterposition difference PD3 653 c in the “iteration 2” diagram, PD3nevertheless corresponds to a substantial difference for HIPP3. Furtheriterations could reduce the difference further but it is not sure thatsufficient improvement can be reached, i.e. further HIPPs, if selectedas above, may never result in a difference that reaches or gets belowthe threshold. Instead of spending time on “chasing” a peak positionthrough numerous iterations, a peak position that that may not even bethere to be found and/or will never be considered reliable, it istypically better to stop after a number of iterations even if it is onlyreached a difference that is above the threshold. This number may thusbe a maximum number of iterations and may be predetermined. Hence, if amaximum number of iterations in the second example would be 2, therewould be no more iterations performed after HIPP3 that thus become thelast iteratively provided HIPP when starting from the initial HIPP, hereHIPP1 in FIG. 6 .

If the last HIPP, e.g. HIPP3, is associated with its difference, thedifference can be used as a quality measure, or reliability and/orunreliability indicator, regarding the last HIPP.

It can be realized that already the difference for HIPP1 could be usedas such reliability indicator or quality measure, which then may be fora peak position found according to a conventional peak findingalgorithm. Thus embodiments herein cannot only be used to accomplishimproved and more correct peak positions than possible throughconventional peak finding algorithms, but can additionally oralternatively be used to find out about reliability or quality of a peakposition determined according to a conventional peak finding algorithm,or determined based on embodiments herein with iterations. Such qualitymeasures can be used to evaluate whether or not to use a determined peakposition, or to what degree it shall be used, to provide a 3D image ormodel of the measure object.

When there are several determined peak positions, such as determinedthrough conventional peak finding algorithm(s) and/or through iterationsas above, e.g. for a complete measure object and a complete space timevolume, a measure, such as a difference mentioned above, can be providedand associated with each determined position and there will thus be aquality measure or reliability indicator for each determined peakposition. These measures or indicators can then be used to determinewhich determined peak positions to use, or to what degree, e.g. throughweighting, when the determined positions are to be used to provide a 3Dimage or model of the measure object.

FIGS. 7A-C are flowcharts schematically illustrating embodiments of amethod based on the above and according to embodiments herein. Theactions below, which may form the method, are for determininginformation regarding an intensity peak position in a space-time volume(STV) formed by image frames. The image frames are generated by an imagesensor, e.g. the image sensor 331, from sensing of light reflected froma measure object, e.g. the measure object 320, as part of lighttriangulation. The space time volume and image frames may be as in theabove examples, e.g. correspond to the STV 360 or the pSTV 361. Inpractice, and as mentioned above, the space time volume and image frameshere are typically part of and is a portion of a larger space timevolume, e.g. pSTV 361 part of the STV 360 that is formed by a largernumber of image frames imaging the complete measure object. For example,part of larger a sequence of image frames IM1 . . . IMi, where i=1 . . .M, and M is an integer in the magnitude of hundred or more. The lighttriangulation should, as in the prior art and conventional lighttriangulation, be performed under known operating conditions. Said lighttriangulation as such may be as in the prior art and thus involvesmovement of at least a light source, e.g. the light source 310 and/orthe measure object 320 in relation to each other, so that at differentconsecutive time instants, different consecutive portions of the measureobject are illuminated by the light source. Reflected light from themeasure object is sensed by the image sensor. The light from the lightsource may be as in conventional light triangulation, for examplestructured light, such as a light line and/or laser light. In lighttriangulation, typically, but not necessary, the camera 330 and lightsource 310 are fixed in relation to each other and the measure objectmove in relation to these. It is also e.g. possible to move e.g. thelight source with or without the camera. Through said sensing by theimage sensor 331, respective image frame IMi of the STV is associatedboth with a respective such time instant, e.g. ti, and with a respectiveportion of the measure object 320 from which the image sensor 331 sensedlight at the respective time instant ti. The STV is further associatedwith space-time trajectories, corresponding to e.g. 262 and 362,relating to how feature points of the measure object map to positions inthe space-time volume. The space time trajectories are thus the samekind of trajectories as defined and used in prior art space timeanalysis and information about, e.g. identifying them, can be obtainedthe same or similarly as in the prior art. This include obtaining themanalytically based on geometries of the measuring system 300, includinginformation given by said known operating conditions, such as in theoriginal space time analysis paper mentioned in the Background, orobtaining them through calibration and use of a reference object asdescribed in EP 2 063 220 B1 also mentioned in the Background.

The method and/or actions below and indicated in FIGS. 5A-C may beperformed by device(s), i.e. one or more devices, such as the camera 330and/or the computing device 301, or by the imaging system 300 and/orsuitable device(s) thereof, or connected to it. Device(s) for performingthe method and actions thereof are further described below.

Note that the actions below may be taken in any suitable order and/or becarried out fully or partly overlapping in time when this is possibleand suitable.

Action 701

A first hypothetical intensity peak position (HIPP1) in said STV isobtained. The HIPP1 may here and in the following be exemplified by theHIPP1 551 a or HIPP1 651 a. In some embodiments, the HIPP1 is in a lineof pixels of an image frame part of said STV. That is, the HIPP1corresponds to a point in three dimensions, such as in (u, v, t)coordinates, which point belong to a position in a certain image frameand to a time instant associated with when the image data of thisposition was captured, corresponding to when the light resulting in theimage data was sensed by the image sensor. Typically there is one andthe same time instant associated with all positions part of the sameimage frame. For example, as above, an image frame IMi is associatedwith a time ti etc. The HIPP1 is advantageously selected in the line ofpixels, preferably as a position with high or at least higherprobability than other positions in the line to be or be close to anactual, i.e. real, intensity peak position. Preferably it is selected bymeans of and/or based on a conventional peak finding algorithm.

Action 702

A first space time analysis position (STAP1) is computed based onspace-time analysis performed locally around HIPP1 and along a firstspace time trajectory that is a space time trajectory of said space timetrajectories that is associated with the HIPP1. The STAP1 may here andin the following be exemplified by the STAP1 552 a or STAP1 652 a.

As used herein, performance locally around a hypothetical intensity peakposition refers to that the space time analysis is performed in apartial space time volume, i.e. pSTV, along a partial space timetrajectory therein. The pSTV may here and in the following beexemplified by the pSTV 361 and the partial space time trajectory by thepartial space time trajectory 362. Said pSTV thus being comprised in agreater space time volume, e.g. the STV 360, imaging the completemeasure object, e.g. the measure object 320. The space time analysis canthus be understood to be performed in the pSTV and using image data fromimages frames forming the pSTV, which are only part, or subsequence, ofimage frames forming the greater STV. Typically this means that thespace time analysis uses image data from a subsequence of image framesassociated with a time interval in the greater space-time volume,covering some image frames from both before and after the HIPP, forexample, but not necessary, symmetrically around the hypothetical peakposition.

As used herein, and as should be realized from the description andexamples herein, space time analysis position (STAP) refers to a centerposition of a light, e.g. intensity, distribution in a space-time volumealong a space-time trajectory in that space-time volume. A STAP can thusbe determined, such as found or identified, by computation based onspace-time analysis, i.e. analysis in space-time, along a space-timetrajectory in the space-time volume, such as in the prior art mentionedthe Background. Given a space time volume and a space-time trajectory inthat space-time volume any STAP along the space-time trajectory in thespace-time volume can be computed, i.e. be determined by computation,based on the same principles and/or methods as in the prior artregarding similar space-time analysis, such as mentioned in theBackground. The STAP may thus e.g. be computed based on finding, e.g.identifying, a center position of a light, such as intensity,distribution along the given space-time trajectory in the givenspace-time volume. As should be realized, the light distribution in thespace time volume may be available from image data of image framesforming the space-time volume and interpolation, and can also be basedon knowledge of expected or known type of light distribution, e.g.Gaussian distribution, and/or expected or known shape of lightdistribution.

Action 703

Said information regarding the intensity peak position is determinedbased on the HIPP1 and the computed STAP1.

Action 704

In some embodiments, Action 703, i.e. the determination of theinformation regarding the intensity peak position, comprises to providea comparison between the HIPP1 and the computed STAP1 as a reliabilityindicator indicating how reliable the HIPP1 is as intensity peakposition. The comparison may be such position difference, PD, mentionedelsewhere herein, but it is realized that also other comparisons can beused or provided, for example simply a set of coordinates of both HIPP1and STAP1 in the STV, all or some coordinates that differ, differenceper each of one or many coordinates, one or more ratios between HIPP1and STAP1 coordinates etc.

These embodiments, may be particularly advantageous when HIPP1 has beendetermined by a conventional peak finding algorithm as explained abovesince it provides valuable information about how good or bad thealgorithm was in finding a reliable peak position and/or for identifyinga problematic peak position so it e.g. can be excluded and not used, orbe rectified and/or replaced. The greater difference indicated by thecomparison, the less reliable peak position, while indicated smaller orno substantial difference, the more reliable peak position.

Action 705

In some embodiments, Action 703, i.e. the determination of theinformation regarding the intensity peak position, comprises to computea first position difference (PD1), e.g. PD1 553 a or PD1 653 a, in thespace-time volume between the HIPP1 and the computed STAP1.

Action 706

In some embodiments, it is checked if the computed PD1 is above or belowa certain threshold value.

If the computer PD1 equals the threshold value it is a matter ofdefinition, and implementation, if actions to be taken should be thesame as if the computer PD1 is below or above the threshold value.

Action 707

In some embodiments, if the computed PD1 is not above the threshold ande.g. below the threshold, the HIPP1 is provided as a determinedintensity peak position.

In other words, Actions 706-707 can be summarized as the HIPP1 may beprovided as a determined intensity peak position if the computed PD1 isbelow a certain threshold value.

Further, in some embodiments, if the computed PD1 is above said certainthreshold value, some or all of Actions 708-714 may be performed atleast once, starting with n=2:

Action 708

In some embodiments, another, new, n:th HIPP is obtained along a n−1:thspace time trajectory and closer to the computed n−1:th STAP than then−1:th HIPP.

Hence, for example:

In a first iteration where n=2, a HIPP2, e.g. HIPP2 551 b or HIPP2 651b, is obtained along the first space time trajectory, i.e. the one usedin Action 702, and closer to the computed STAP1, e.g. STAP1 552 a orSTAP1 652 a, than the HIPP1, e.g. HIPP1 551 a or HIPP1 651 a.

In a second iteration where n=3, thus after Actions 708-711 first havebeen performed for n=2, a HIPP3, e.g. HIPP3 551 c or HIPP3 651 c, isobtained along the second space time trajectory, i.e. a space timetrajectory of said space time trajectories and that is associated withthe HIPP2 and that was used to compute a STAP2 in Action 809 during thefirst iteration. The HIPP3 is obtained, e.g. selected, closer to thecomputed STAP2 than the HIPP2, e.g. HIPP2 551 b or HIPP2 651 b.

Etc.

Action 709

In some embodiments, an n:th STAP is computed based on space-timeanalysis performed locally around the n:th HIPP and along a n:th spacetime trajectory. The n:th space time trajectory is a space timetrajectory of said space time trajectories that is associated with then:th hypothetical intensity peak position.

Hence, for example:

In the first iteration where n=2, a STAP2, e.g. STAP2 552 b or STAP2 652b, is computed based on space-time analysis performed locally around theHIPP2, e.g. HIPP2 551 b or HIPP2 651 b, and along a second space timetrajectory. The second space time trajectory being a space timetrajectory of said space time trajectories that is associated with theHIPP2.

In the second iteration where n=3, thus after Actions 708-711 first havebeen performed for n=2, a STAP3, e.g. STAP3 552 c or STAP3 652 c, iscomputed based on space-time analysis performed locally around theHIPP3, e.g. HIPP3 551 c or HIPP3 651 c, and along a third space timetrajectory. The third space time trajectory being a space timetrajectory of said space time trajectories that is associated with theHIPP3.

Etc.

Action 710

In some embodiments, an n:th PD is computed. The n:th PD is a differencebetween the n:th hypothetical intensity peak position and the computedn:th space time peak position.

Hence, for example:

In the first iteration where n=2, a PD2, e.g. PD2 553 b or PD2 653 b, iscomputed. The PD2 being a difference between the HIPP2, e.g. HIPP2 551 bor HIPP2 651 b, and the computed STAP2, e.g. 552 b or 652 b.

In the second iteration where n=3 (thus after Actions 708-711 first havebeen performed for n=2), a PD3, e.g. PD3 553 c or PD3 653 b, iscomputed. The PD3 being a difference between the HIPP3, e.g. HIPP3 551 cor HIPP3 651 c, and the computed STAP3, e.g. 552 c or 652 c.

Etc.

Action 711

In some embodiments, it is checked if the computed n:th PD is above orbelow said certain threshold value, i.e. same threshold as used inAction 706.

If the computed n:th PD equals the threshold value it is a matter ofdefinition and implementation if actions to be taken should be the sameas if the computed PD1 is below or above the threshold value.

In some embodiments, if the computed n:th PD is above, or in some ofthese embodiments equal to, said certain threshold value, anotheriteration starting with Action 708 may take part, now with n=n+1, orAction 712 may first be performed.

In some embodiments, if the computed n:th PD instead is below, or insome of these embodiments and/or equal to, said certain threshold value,Action 713 and/or Action 714 are performed.

Action 712

In some embodiments, it is checked if n is below a predefined, orpredetermined, integer N, or it is checked that n is below or equal toN. Note that if n equals N it is a matter of definition andimplementation if actions to be taken should be the same as if n isbelow or above N.

For example, say that N=3, it is checked if n is below N and it is thefirst iteration, i.e. iteration 1, where n=2. This will thus result inthat n is below N, but for iteration 2 where n=3 this is would no longerbe the case.

In some embodiments, if n is below, or in some of these embodimentsequal to, N, another iteration starting with Action 708 may take part,now with n=n+1.

In some embodiments, if n instead is above, or in some of theseembodiments equal to, N, Action 713 and/or Action 714 are performed.

Note that in some embodiments, not illustrated by the figure, Actions712 and 711 are in reverse order, e.g. have swapped place with eachother, although it may be beneficial to, as shown in the figure, firstcheck against the threshold.

Action 713

The n:th, corresponding to a last obtained, HIPP is provided as adetermined intensity peak position, i.e. similar as in Action 707 butnow for another HIPP after one or more improving iterations.

For example:

If this happens in the first iteration where n=2, the HIPP2 is providedas the determined intensity peak position.

If this instead happens in the second iteration where n=3, the HIPP3 isprovided as the determined intensity peak position.

In some embodiments, if n is equal to, or above, the predefined integerN, as checked in Action 712, the last HIPP, i.e. the n:th HIPP, isassociated with unreliability. That is, the n:th HIPP may be provided asdetermined intensity peak position after one or more iterations even ifthe n:th PD is above the threshold, but is then associated withunreliability, i.e. that the n:th HIPP is unreliable, e.g. that the n:thHIPP is not or is likely not an actual or accurate peak position, sincethe n:th PD was below the threshold even after one or more iterations.

Action 714

In some embodiments, the last computed PD, i.e. the first or n:th PDwhen iterations have stopped, is provided as a reliability indicator ofthe determined intensity peak position, i.e. of the HIPP that wasprovided as the determined intensity peak position in Action 707 orAction 713.

Part of what has been disclosed above in connection with Actions 711-714can be summarized as follows:

The n:th HIPP may be provided as a determined intensity peak position ifthe computed n:th PD is below, and/or equal to, said certain thresholdvalue. In that case no more iterations are performed.

If the computed n:th PD instead is above said certain threshold, anotheriteration may be performed with n=n+1, i.e. some or all of Actions708-712 may be performed again but now with n=n+1. Hence after the firstiteration 1 with n=2, the second iteration 2 may be performed with n=3.In some embodiments for this to happen, i.e. perform another iteration,it is also required that n is below, or below or equal to, thepredefined integer N>2, i.e. as checked in Action 712.

Note that for efficiency and simplified implementation, the positiondifferences, i.e. PDs, in the examples above may be computed only fortime coordinates, i.e. only be a position difference in time, i.e. timedifference, between positions in the space time volume. In otherembodiments, differences involving all coordinates, or othercoordinate(s), than time are computed and used.

FIGS. 8A-B show a result from light triangulation when a conventionalpeak finding algorithm has been used for qualitative comparison with aresult when embodiments herein have been applied. The same imagesequence has been used in both cases, corresponding to output from alight triangulation imaging system as described above.

FIG. 8A is the result when peak positions found by a conventional peakfinding algorithm have been used to form the shown image. Intensity isin the shown image proportional to height or depth. The imaged pyramidcan be seen to have some variations on some of the sloped surfaces dueto that the conventional peak finding algorithm have had problems todeal with a checker pattern on these sloped surfaces.

FIG. 8B, the upper picture, is the result when peak positions have beendetermined by iteratively and locally applying space time analysisaccording to embodiments herein and then the shown figure has beenproduced based on the determined positions. There is no identifiable andundesirable variation on the sloped surfaces as in FIG. 8A, and at leastto a much lower extent than in FIG. 8A. Embodiments herein can thusprovide better results and 3D images than conventional algorithms.

FIG. 8B, the lower part, illustrates resulting position differences(PDs) in time for the last HIPPs, i.e. the ones used to determine thepeak positions that the upper image of FIG. 8A is based on. This is anexample that corresponds to Action 714. Intensity is in the shown imageproportional to size of the difference. Some areas of low intensityvariations can be identified, thus corresponding to determined peakpositions according to embodiments herein with some but relatively smallposition difference between HIPP and STAP. The variations anddifferences are especially on the surfaces where the conventional peakfinding algorithm had even greater problems, which is expected.

Please note that there is an error on the right side of the pyramid, inboth FIG. 8A with result from conventional peak finding in FIG. 8A andthe upper picture of FIG. 8B with result based on embodiments herein.This could e.g. be due to some undesired reflections when the imagesused were captured. This also causes a clear indication of the problemin the lower position difference picture of FIG. 8B. Hence, if theresulting position differences are used as indication of reliability ofdetermined peak positions, the positions with this error can easily beidentified in the upper 3D image of FIG. 8B and that data be excluded inthe 3D image, or marked up, or replaced by values interpolated fromsurrounding positions, depending on what is desirable and suitabledepending on what the 3D image is to be used for.

FIG. 9 is a schematic block diagram for illustrating embodiments of oneor more devices 900, i.e. device(s) 900, that may correspond todevices(s) already mention in the above for performing embodimentsherein, such as for performing the method and/or actions described abovein relation to FIGS. 7A-C. The device(s) may e.g. correspond to thecomputing device 301 and/or the camera 330, or the devices forming theimaging system 300.

The schematic block diagram is for illustrating embodiments regardinghow the device(s) 900 may be configured to perform the method andactions discussed above in relation to FIGS. 7A-C. Hence, the device(s)900 is for determining information regarding said intensity peakposition in said space-time volume formed by said image frames. Theimage frames generated by said image sensor 331 from sensing of lightreflected from said measure object as part of light triangulation. Thatis, as already described above for FIGS. 7A-C, where the lighttriangulation involves movement of said at least light source 310 and/orsaid measure object 320 in relation to each other, so that at differentconsecutive time instants, different consecutive portions of the measureobject 320 are illuminated by the light source 310 and reflected lightfrom the measure object 320 is sensed by the image sensor. Through saidsensing by the image sensor 331, each image frame of the space timevolume is associated both with a respective such time instant, e.g. t,and with a respective portion of the measure object 320 from which theimage sensor 331 sensed light at the respective time instant. Saidspace-time volume is further associated with said space-timetrajectories relating to how feature points of the measure object map topositions in the space-time volume.

The device(s) 900 may comprise a processing module 901, such asprocessing means, one or more hardware modules, including e.g. one ormore processing circuits, circuitry, such as processors, and/or one ormore software modules for performing said method and/or actions.

The device(s) 900 may further comprise memory 902 that may comprise,such as contain or store, a computer program 903. The computer program903 comprises ‘instructions’ or ‘code’ directly or indirectly executableby the device(s) 900 to perform said method and/or actions. The memory902 may comprise one or more memory units and may further be arranged tostore data, such as configurations, data and/or values, involved in orfor performing functions and actions of embodiments herein.

Moreover, the device(s) 900 may comprise processing circuitry 904involved in processing and e.g. encoding data, as exemplifying hardwaremodule(s) and may comprise or correspond to one or more processors orprocessing circuits. The processing module(s) 901 may comprise, e.g. ‘beembodied in the form of’ or ‘realized by’ the processing circuitry 904.In these embodiments, the memory 902 may comprise the computer program903 executable by the processing circuitry 1204, whereby the device(s)900 is operative, or configured, to perform said method and/or actionsthereof.

Typically the device(s) 900, e.g. the processing module(s) 901,comprises an Input/Output (I/O) module(s) 905, configured to be involvedin, e.g. by performing, any communication to and/or from other unitsand/or devices, such as sending and/or receiving information to and/orfrom other devices. The I/O module(s) 905 may be exemplified byobtaining, e.g. receiving, module(s) and/or providing, e.g. sending,module(s), when applicable.

Further, in some embodiments, the device(s) 900, e.g. the processingmodule(s) 901, comprises one or more of obtaining module(s), computingmodule(s), determining modules(s), performing module(s), associatingmodule(s), and providing module(s), as exemplifying hardware and/orsoftware module(s) for carrying out actions of embodiments herein. Thesemodules may be fully or partly implemented by the processing circuitry904.

Hence:

The device(s) 900, and/or the processing module(s) 901, and/or theprocessing circuitry 904, and/or the I/O module(s) 905, and/or theobtaining module(s) may be operative, or configured, to, obtain saidfirst hypothetical intensity peak position, HIPP1, in said space timevolume (STV).

The device(s) 900, and/or the processing module(s) 901, and/or theprocessing circuitry 904, and/or the computing module(s) may beoperative, or configured, to compute said first space time analysisposition, STAP1, based on the space-time analysis performed locallyaround said first hypothetical intensity peak position, HIPP1, and alongsaid first space time trajectory.

The device(s) 900, and/or the processing module(s) 901, and/or theprocessing circuitry 904, and/or the determining module(s) may beoperative, or configured, to determine said information regarding theintensity peak position based on the first hypothetical intensity peakposition, HIPP1, and the computed first space time analysis position,STAP 1.

In some embodiments, the device(s) 900, and/or the processing module(s)901, and/or the processing circuitry 904, and/or the computing module(s)are operative, or configured, to compute said first position difference,PD1.

In some embodiments, the device(s) 900, and/or the processing module(s)901, and/or the processing circuitry 904, and/or the I/O module(s) 905,and/or the providing module(s) are operative, or configured, to, if saidcomputed PD1 is below said certain threshold value, provide the HIPP1 asthe determined intensity peak position.

In some embodiments, the device(s) 900, and/or the processing module(s)901, and/or the processing circuitry 904, and/or the I/O module(s) 905,and/or the obtaining module(s), and/or the computing module(s), and/orthe providing module(s),are operative, or configured, to, if thecomputed PD1 is above said certain threshold value and for at least,and/or starting with, n=2, obtain said another, new, n:th HIPP,

compute said n:th space time analysis position, STAP,

compute said n:th position difference, PD, and

provide said n:th HIPP as the a determined intensity peak position ifthe computed n:th PD is below said certain threshold value.

In some embodiments, the device(s) 900, and/or the processing module(s)901, and/or the processing circuitry 904, and/or the providing module(s)and/or the associating module(s) are operative, or configured, to, if nis above or equal to the predefined integer N, provide the n:th HIPP asthe determined intensity peak position and associate the determinedintensity peak position with said unreliability.

In some embodiments, the device(s) 900, and/or the processing module(s)901, and/or the processing circuitry 904, the providing module(s),and/or the I/O module(s) 905, are operative, or configured, to providesaid last computed PD as a reliability indicator of the determinedintensity peak position.

Moreover, in some embodiments, the device(s) 900, and/or the processingmodule(s) 901, and/or the processing circuitry 904, the providingmodule(s), and/or the I/O module(s) 905, are operative, or configured,to provide said comparison between the HIPP1 and the computed STAP1 assaid reliability indicator.

FIG. 10 is a schematic drawing illustrating some embodiments relating tocomputer program and carriers thereof to cause said device(s) 900discussed above to perform said method and actions.

The computer program may be the computer program 903 and comprisesinstructions that when executed by the processing circuitry 904 and/orthe processing module(s) 901, cause the device(s) 900 to perform asdescribed above. In some embodiments there is provided a carrier, ormore specifically a data carrier, e.g. a computer program product,comprising the computer program. The carrier may be one of an electronicsignal, an optical signal, a radio signal, and a computer readablestorage medium, e.g. a computer readable storage medium 1001 asschematically illustrated in the figure. The computer program 903 maythus be stored on the computer readable storage medium 1001. By carriermay be excluded a transitory, propagating signal and the data carriermay correspondingly be named non-transitory data carrier. Non-limitingexamples of the data carrier being a computer readable storage medium isa memory card or a memory stick, a disc storage medium such as a CD orDVD, or a mass storage device that typically is based on hard drive(s)or Solid State Drive(s) (SSD). The computer readable storage medium 1001may be used for storing data accessible over a computer network 1002,e.g. the Internet or a Local Area Network (LAN). The computer program903 may furthermore be provided as pure computer program(s) or comprisedin a file or files. The file or files may be stored on the computerreadable storage medium 1001 and e.g. available through download e.g.over the computer network 1002 as indicated in the figure, e.g. via aserver. The server may e.g. be a web or File Transfer Protocol (FTP)server. The file or files may e.g. be executable files for direct orindirect download to and execution on said device(s) to make it performas described above, e.g. by execution by the processing circuitry 904.The file or files may also or alternatively be for intermediate downloadand compilation involving the same or another processor(s) to make themexecutable before further download and execution causing said device(s)900 to perform as described above.

Note that any processing module(s) and circuit(s) mentioned in theforegoing may be implemented as a software and/or hardware module, e.g.in existing hardware and/or as an Application Specific IntegratedCircuit (ASIC), a Field-Programmable Gate Array (FPGA) or the like. Alsonote that any hardware module(s) and/or circuit(s) mentioned in theforegoing may e.g. be included in a single ASIC or FPGA, or bedistributed among several separate hardware components, whetherindividually packaged or assembled into a System-on-a-Chip (SoC).

Those skilled in the art will also appreciate that the modules andcircuitry discussed herein may refer to a combination of hardwaremodules, software modules, analogue and digital circuits, and/or one ormore processors configured with software and/or firmware, e.g. stored inmemory, that, when executed by the one or more processors may make thedevice(s), sensor(s) etc. to be configured to and/or to perform theabove-described methods and actions.

Identification by any identifier herein may be implicit or explicit. Theidentification may be unique in a certain context, e.g. for a certaincomputer program or program provider.

As used herein, the term “memory” may refer to a data memory for storingdigital information, typically a hard disk, a magnetic storage, medium,a portable computer diskette or disc, flash memory, Random Access Memory(RAM) or the like. Furthermore, the memory may be an internal registermemory of a processor.

Also note that any enumerating terminology such as first device, seconddevice, first surface, second surface, etc., should as such beconsidered non-limiting and the terminology as such does not imply acertain hierarchical relation. Without any explicit information in thecontrary, naming by enumeration should be considered merely a way ofaccomplishing different names.

As used herein, the expression “configured to” may mean that aprocessing circuit is configured to, or adapted to, by means of softwareor hardware configuration, perform one or more of the actions describedherein.

As used herein, the terms “number” or “value” may refer to any kind ofdigit, such as binary, real, imaginary or rational number or the like.Moreover, “number” or “value” may be one or more characters, such as aletter or a string of letters. Also, “number” or “value” may berepresented by a bit string.

As used herein, the expression “may” and “in some embodiments” hastypically been used to indicate that the features described may becombined with any other embodiment disclosed herein.

In the drawings, features that may be present in only some embodimentsare typically drawn using dotted or dashed lines.

When using the word “comprise” or “comprising” it shall be interpretedas nonlimiting, i.e. meaning “consist at least of”.

The embodiments herein are not limited to the above describedembodiments. Various alternatives, modifications and equivalents may beused. Therefore, the above embodiments should not be taken as limitingthe scope of the present disclosure, which is defined by the appendingclaims.

We claim:
 1. Method for determining information regarding an intensitypeak position in a space-time volume (360; 361) formed by image framesgenerated by an image sensor (331) from sensing of light reflected froma measure object (320) as part of light triangulation, wherein saidlight triangulation is based on movement of at least a light source(310) and/or the measure object (320) in relation to each other so thatat different consecutive time instants, different consecutive portionsof the measure object (320) are illuminated by the light source (310)and reflected light from the measure object (320) is sensed by the imagesensor (331) whereby each image frame of the space time volume (360 361)is associated both with a respective such time instant and with arespective portion of the measure object (320) from which the imagesensor (331) sensed light at the respective time instant, wherein saidspace-time volume (360; 361) is further associated with space-timetrajectories relating to how feature points of the measure object (320)map to positions in the space-time volume (360; 361), wherein the methodcomprises: obtaining (701) a first hypothetical intensity peak position(551 a; 651 a) in said space time volume (360; 361), computing (702) afirst space time analysis position (552 a; 652 a) based on space-timeanalysis performed locally around the first hypothetical intensity peakposition (551 a; 651 a) and along a first space time trajectory that isa space time trajectory of said space time trajectories that isassociated with the first hypothetical intensity peak position (551 a,651 a), and determining (703) said information regarding the intensitypeak position based on the first hypothetical intensity peak position(551 a; 651 a) and the computed first space time analysis position (552a; 652 a).
 2. The method as claimed in claim 1, wherein the firsthypothetical intensity peak position (551 a; 651 a) is in a line ofpixels of an image frame that is part of said space-time volume (360;361).
 3. The method as claimed in claim 1, wherein said determination(703) of the information regarding the intensity peak positioncomprises: computing (705) a first position difference (553 a; 653 a) inthe space-time volume between the first hypothetical intensity peakposition (551 a, 651 a) and the computed first space time analysisposition (552 a; 652 a), and if the computed first position difference(553 a; 653 a) is below a certain threshold value, providing (707) thefirst hypothetical intensity peak position (551 a, 651 a) as adetermined intensity peak position.
 4. The method as claimed in claim 3,wherein said determination of the information regarding the intensitypeak position further comprises: if the computed first positiondifference (553 a; 653 a) is above said certain threshold value,performing the following actions at least once and starting with n=2, a)obtaining (708) another, new, n:th hypothetical intensity peak position(551 b; 551 c; 651 b; 651 c) along the n−1:th space time trajectory andcloser to the computed n−1:th space time analysis position (552 a; 552b; 652 a; 652 b) than the n−1:th hypothetical intensity peak position(551 a; 551 b; 651 a; 651 b), b) computing (709) an n:th space timeanalysis position (552 b; 552 c; 652 b; 652 c) based on space-timeanalysis performed locally around the n:th hypothetical intensity peakposition (551 b; 551 c; 651 b; 651 c) and along a n:th space timetrajectory that is a space time trajectory of said space timetrajectories that is associated with the n:th hypothetical intensitypeak position (551 b; 551 c; 651 b; 651 c), c) computing (710) an n:thposition difference (553 b; 553 c; 653 b; 653 c) in the space-timevolume between the n:th hypothetical intensity peak position (551 b; 551c; 651 b; 651 c) and the computed n:th space time analysis position (552b; 552 c; 652 b; 652 c), d) if the computed n:th position difference(553 b; 553 c; 653 b; 653 c) is below said certain threshold value,providing (713) the n:th hypothetical intensity peak position (551 b;551 c; 651 b; 651 c) as the determined intensity peak position, and e)if the computed n:th position difference (553 b; 553 c; 653 b; 653 c) isabove said certain threshold value, performing actions a-e) again withn=n+1.
 5. The method as claimed in claim 4, wherein action e) comprisesto perform action a-e) again only if also n is below a predefinedinteger N>2.
 6. The method as claimed in claim 5, wherein action e)further comprises: if n is above or equal to the predefined integer N,providing (713) the n:th hypothetical intensity peak position (551 b;551 c; 651 b; 651 c) as the determined intensity peak position andassociating the n:th hypothetical intensity peak position withunreliability.
 7. The method as claimed in claim 3, wherein saiddetermination of the information regarding the intensity peak positionfurther comprises: providing (707; 714) the last computed positiondifference (553 a; 553 b; 553 c; 653 a; 653 b; 653 c) as a reliabilityindicator of the determined intensity peak position.
 8. The method asclaimed in claim 1, wherein said determination of the informationregarding the intensity peak position comprises: providing (704) acomparison between the first hypothetical intensity peak position (551a; 651 a) and the computed first space time analysis position (552 a;652 a) as a reliability indicator indicating how reliable the firsthypothetical intensity peak position (551 a; 651 a) is as intensity peakposition.
 9. A computer program (903) comprising instructions that whenexecuted by one or more processors (904) causes one or more devices(900) to perform the method according to claim
 1. 10. A carriercomprising the computer program (903) according to claim 9, wherein thecarrier is one of an electronic signal, optical signal, radio signal orcomputer readable storage medium (901).
 11. One or more devices (900;301; 330; 300) for determining information regarding an intensity peakposition in a space-time volume (360; 361) formed by image framesgenerated by an image sensor (331) from sensing of light reflected froma measure object (320) as part of light triangulation, wherein saidlight triangulation is based on movement of at least a light source(310) and/or the measure object (320) in relation to each other so thatat different consecutive time instants, different consecutive portionsof the measure object (320) are illuminated by the light source (310)and reflected light from the measure object (320) is sensed by the imagesensor (331) whereby respective image frame of the space time volume(360; 361) is associated both with a respective such time instant andwith a respective portion of the measure object (320) from which theimage sensor (331) sensed light at the respective time instant, whereinsaid space-time volume (360; 361) is further associated with space-timetrajectories relating to how feature points of the measure object (320)map to positions in the space-time volume (360; 361), wherein said oneor more devices are configured to: obtain (701) a first hypotheticalintensity peak position (551 a; 651 a) in said space time volume (360;361), compute (702) a first space time analysis position (552 a; 652 a)based on space-time analysis performed locally around the firsthypothetical intensity peak position (551 a; 651 a) and along a firstspace time trajectory that is a space time trajectory of said space timetrajectories that is associated with the first hypothetical intensitypeak position (551 a, 651 a), and determine (703) said informationregarding the intensity peak position based on the first hypotheticalintensity peak position (551 a; 651 a) and the computed first space timeanalysis position (552 a; 652 a).
 12. The one or more devices as claimedin claim 11, wherein the first hypothetical intensity peak position (551a; 651 a) is in a line of pixels of an image frame that is part of saidspace-time volume (360; 361).
 13. The one or more devices as claimed inclaim 11, wherein said one or more devices being configured to determinethe information regarding the intensity peak position comprises that theone or more device are configured to: compute (705) a first positiondifference (553 a; 653 a) in the space-time volume between the firsthypothetical intensity peak position (551 a, 651 a) and the computedfirst space time analysis position (552 a; 652 a), and if the computedfirst position difference (553 a; 653 a) is below a certain thresholdvalue, provide (707) the first hypothetical intensity peak position (551a, 651 a) as a determined intensity peak position.
 14. The one or moredevices as claimed in claim 13, wherein said one or more devices beingconfigured to determine the information regarding the intensity peakposition further comprises that the one or more devices are configuredto: if the computed first position difference (553 a; 653 a) is abovesaid certain threshold value, at least once and starting with n=2, a)obtain (708) another, new, n:th hypothetical intensity peak position(551 b; 551 c; 651 b; 651 c) along the n−1:th space time trajectory andcloser to the computed n−1:th space time analysis position (552 a; 552b; 652 a; 652 b) than the n−1:th hypothetical intensity peak position(551 a; 551 b; 651 a; 651 b), b) compute (709) an n:th space timeanalysis position (552 b; 552 c; 652 b; 652 c) based on space-timeanalysis performed locally around the n:th hypothetical intensity peakposition (551 b; 551 c; 651 b; 651 c) and along a n:th space timetrajectory that is a space time trajectory of said space timetrajectories that is associated with the n:th hypothetical intensitypeak position (551 b; 551 c; 651 b; 651 c), c) compute (710) an n:thposition difference (553 b; 553 c; 653 b; 653 c) in the space-timevolume between the n:th hypothetical intensity peak position (551 b; 551c; 651 b; 651 c) and the computed n:th space time analysis position (552b; 552 c; 652 b; 652 c), d) if the computed n:th position difference(553 b; 553 c; 653 b; 653 c) is below said certain threshold value,provide (713) the n:th hypothetical intensity peak position (551 b; 551c; 651 b; 651 c) as the determined intensity peak position, and e) ifthe computed n:th position difference (553 b; 553 c; 653 b; 653 c) isabove said certain threshold value, perform a-e) again with n=n+1. 15.The one or more devices as claimed in claim 11, wherein said one or moredevices being configured to determine the information regarding theintensity peak position comprises that the one or more device areconfigured to: provide (704) a comparison between the first hypotheticalintensity peak position (551 a; 651 a) and the computed first space timeanalysis position (552 a; 652 a) as a reliability indicator indicatinghow reliable the first hypothetical intensity peak position (551 a; 651a) is as intensity peak position.